PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916949494071296 |
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| author | Liu, Junyu Jones, R. Kenny Ritchie, Daniel |
| author_facet | Liu, Junyu Jones, R. Kenny Ritchie, Daniel |
| contents | We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between denoised latents and structured concept codes via a concept predictor, enabling direct regulation on concept disentanglement and re-composition supervision. Our method achieves strong disentanglement and controllable composition, outperforming subject and part-level baselines when mixing concepts from the same, or different, object categories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples Liu, Junyu Jones, R. Kenny Ritchie, Daniel Graphics Computer Vision and Pattern Recognition We present PartComposer: a framework for part-level concept learning from single-image examples that enables text-to-image diffusion models to compose novel objects from meaningful components. Existing methods either struggle with effectively learning fine-grained concepts or require a large dataset as input. We propose a dynamic data synthesis pipeline generating diverse part compositions to address one-shot data scarcity. Most importantly, we propose to maximize the mutual information between denoised latents and structured concept codes via a concept predictor, enabling direct regulation on concept disentanglement and re-composition supervision. Our method achieves strong disentanglement and controllable composition, outperforming subject and part-level baselines when mixing concepts from the same, or different, object categories. |
| title | PartComposer: Learning and Composing Part-Level Concepts from Single-Image Examples |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.03004 |